We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual ... [more ▼]

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence of uniform DIF, non uniform DIF, or both. This generalized procedure is compared to other existing DIF methods for multiple groups with a real data set on language skill assessment. Emphasis is put on the flexibility, completeness and computational easiness of the generalized method. [less ▲]

Computerized adaptive testing (CAT) is an active current research field in psychometrics and educational measurement. However, there is very little software available to handle such adaptive tasks. The R ... [more ▼]

Computerized adaptive testing (CAT) is an active current research field in psychometrics and educational measurement. However, there is very little software available to handle such adaptive tasks. The R package catR was developed to perform adaptive testing with as much flexibility as possible, in an attempt to provide a developmental and testing platform to the interested user. Several item-selection rules and ability estimators are implemented. The item bank can be provided by the user or randomly generated from parent distributions of item parameters. Three stopping rules are available. The output can be graphically displayed. [less ▲]

In this paper, the estimation of extremely large or extremely small proficiency levels, given the item parameters of a logistic item response model, is investigated. On the one hand, the estimation of ... [more ▼]

In this paper, the estimation of extremely large or extremely small proficiency levels, given the item parameters of a logistic item response model, is investigated. On the one hand, the estimation of proficiency levels by maximum likelihood (ML), despite being asymptotically unbiased, may yield infinite estimates. On the other hand, with an appropriate prior distribution, the Bayesian approach of maximum a posteriori (MAP) yields finite estimates, but it suffers from severe estimation bias at the extremes of the proficiency scale. In a first step, we propose a simple correction to the MAP estimator in order to reduce this estimation bias. The correction factor is determined through a simulation study and depends only on the length of the test. In a second step, some additional simulations highlight that the corrected estimator behaves like the ML estimator and outperforms the standard MAP method for extremely small or extremely large abilities. Although based on the Rasch model, the method could be adapted to other logistic item response models. [less ▲]

Frequently, candidates at aptitude multiple choice test miss attention, motivation or preparation and show underachievement or overachievement. Similarly, at surveys respondents show frequently misfitting patterns of responses. Their result does not correspond any more to their true aptitude or attitude, an inappropriate response pattern being obtained. New multidimensional models specific to polytomous responses circumvent these situations and diminish considerably the associated person bias. Multidimensional polytomous item response models adding new person parameters to the trait of the candidate are proposed. In the spirit of previous Raiche’s dichotomous responses IRT propositions, like the discrimination and don’t know item parameters, these models offer fluctuation and don’t know person parameters. Estimation methods, results from simulation showing the efficacy of these models and recommendations for the design of testing situations will be presented. [less ▲]

Several authors (Molenaar & Hoijtink, 1990; Meijer & Sijtsma, 2001) have shown that several person-fit statistics present some important limitations. One issue is that the distribution of some parametric ... [more ▼]

Several authors (Molenaar & Hoijtink, 1990; Meijer & Sijtsma, 2001) have shown that several person-fit statistics present some important limitations. One issue is that the distribution of some parametric person-fit statistics is unknown. Another important issue is that the distribution of person-fit indexes is most often derived under the true ability level. In this situation, replacing the true ability by some estimate can seriously affect the distribution of these indexes. Snijders (2001) proposed a method to correct the mean and the variance of many parametric person-fit statistics to be approximately standard normally distributed, and derived the corrected version of the lz index. The purpose of this paper is to apply this correction to other well-known parametric indexes, and to compare them with their classical versions. The simulation results indicate that the standardized indexes have empirical type I errors close to the nominal significance level, and that the corrected indexes outperform their classical versions in this regard. [less ▲]